2 citations · 2 across the 3 of their papers we have counts for
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cs.CL2026
Do Personalized LLMs Need the Whole Conversation? Matching the Full History with Only 5% of Its Text via Relevance-Filtered Context
Erika Hayashi, Andrew Shin
Personalized assistants accumulate long conversation histories, and passing the whole history to an LLM with every request is costly. We show that it is also unnecessary. On Person…
cs.CL2025
Large Language Models' Internal Perception of Symbolic Music
Andrew Shin, Kunitake Kaneko
Large language models (LLMs) excel at modeling relationships between strings in natural language and have shown promise in extending to other symbolic domains like coding or mathem…
cs.CL2024★ 2 cited
Large Language Models Lack Understanding of Character Composition of Words
Andrew Shin, Kunitake Kaneko
Large language models (LLMs) have demonstrated remarkable performances on a wide range of natural language tasks. Yet, LLMs' successes have been largely restricted to tasks concern…